EngageTriBoost: Predictive Modeling of User Engagement in Digital Mental Health Intervention Using Explainable Machine Learning

arXiv cs.LG / 4/13/2026

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Key Points

The study addresses adoption barriers in digital mental health interventions by using machine learning to predict user engagement within eBridge, an online counseling program grounded in motivational interviewing.
An ensemble approach called EngageTriBoost predicts engagement (based on sign-ins and counselor interactions) with performance reported as up to 84% accuracy.
The research applies SHAP explainable AI to identify interpretable drivers of engagement, emphasizing factors such as emotional dysregulation and perceived stigma.
Findings suggest that explainable modeling of engagement can inform strategies to improve DMHI uptake and reduce dropout, potentially improving downstream mental-health outcomes.

Abstract

Mental health challenges among young adults, are on the rise, necessitating effective solutions such as digital mental health interventions (DMHIs). Despite their promise, DMHIs face significant adoption barriers, including low initial uptake and high dropout rates. This study leverages machine learning (ML) to analyze behavioral patterns of users of a DMHI, eBridge, designed to increase the utilization of professional mental health services among at-risk college students through motivational interviewing-based online counseling. Our ensemble model, EngageTriBoost, achieved up to 84% accuracy in predicting engagement, measured by sign-ins and counselor interactions. We then applied the Shapley Additive exPlanations (SHAP) analysis which provided clear, interpretable insights into key factors influencing user engagement such as emotional dysregulation and perceived stigma, highlighting their critical effect on DMHI adoption. This study demonstrates the power of explainable ML for better understanding user engagement with DMHI to improve their adoption and achievable impact on mental health outcomes.